Error mitigation with Clifford quantum-circuit data

Achieving near-term quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data {Xinoisy,Xiexact} via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where Xinoisy and Xiexact are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. Here, we obtain an order-of-magnitude error reduction…

• UD
  U.S. Department of Energy

  Award: 89233218CNA000001
• OO
  Office of Science
• NN
  National Nuclear Security Administration

  Award: 89233218CNA000001
• AS
  Advanced Scientific Computing Research
• LD
  Laboratory Directed Research and Development
• LA
  Los Alamos National Laboratory

  Award: 89233218CNA000001